Detecting attempts to defeat facial recognition

ABSTRACT

A device may select an individual that is a candidate for authentication by facial recognition. The device may identify a facial area of the individual and an area of exposed skin of the individual. The device may obtain a first temperature associated with the facial area of the individual and a second temperature associated with the area of exposed skin of the individual. The device may determine, based on the first temperature and the second temperature, whether an appearance of the facial area of the individual is likely altered by a face-altering technology. The device may selectively perform facial recognition on the facial area of the individual based on whether the appearance of the facial area of the individual is likely altered by the face-altering technology.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/516,117, filed Jul. 18, 2019 (now U.S. Pat. No. 11,450,151), which isincorporated herein by reference in its entirety.

BACKGROUND

A facial recognition technique may identify an individual that isdepicted in an image or a video frame. For example, the facialrecognition technique may select one or more facial features of a facedepicted in the image or the video frame, and compare the selectedfacial features to faces in a database. The database may include amapping of the faces to identities, thereby permitting identification ofthe individual that is depicted in the image or the video frame.

SUMMARY

According to some implementations, a method may include selecting, by adevice, an individual that is a candidate for authentication by facialrecognition; identifying, by the device and based on selecting theindividual, a facial area of the individual and an area of exposed skinof the individual, wherein the area of exposed skin of the individual isnot associated with the facial area of the individual; obtaining, by thedevice, a first temperature associated with the facial area of theindividual and a second temperature associated with the area of exposedskin of the individual; determining, by the device and based on thefirst temperature and the second temperature, whether an appearance ofthe facial area of the individual is likely altered by a face-alteringtechnology, wherein the first temperature corresponding to the secondtemperature indicates that the appearance of the facial area of theindividual is likely not altered by the face-altering technology,wherein the first temperature not corresponding to the secondtemperature indicates that the appearance of the facial area of theindividual is likely altered by the face-altering technology; andselectively performing, by the device, facial recognition on the facialarea of the individual based on whether the appearance of the facialarea of the individual is likely altered by the face-alteringtechnology.

According to some implementations, a device may include one or morememories and one or more processors, communicatively coupled to the oneor more memories, to select an individual that is a candidate forauthentication by facial recognition; obtain a temperature associatedwith a facial area of the individual; determine an estimated temperaturefor the facial area of the individual, wherein the estimated temperatureis based on conditions of an environment associated with the individual;determine, based on the temperature and the estimated temperature,whether an appearance of the facial area of the individual is likelyaltered by a face-altering technology, wherein the temperaturecorresponding to the estimated temperature indicates that the appearanceof the facial area of the individual is likely not altered by theface-altering technology, wherein the temperature not corresponding tothe estimated temperature indicates that the appearance of the facialarea of the individual is likely altered by the face-alteringtechnology; and selectively perform facial recognition on the facialarea of the individual based on whether the appearance of the facialarea of the individual is likely altered by the face-alteringtechnology.

According to some implementations, a non-transitory computer-readablemedium may store one or more instructions that, when executed by one ormore processors of, may cause the one or more processors to select anindividual that is a candidate for authentication by facial recognition;identify, based on selecting the individual, a facial area of theindividual and an area of exposed skin of the individual, wherein thearea of exposed skin of the individual is not associated with the facialarea of the individual; obtain a first temperature associated with thefacial area of the individual and a second temperature associated withthe area of exposed skin of the individual; determine an estimatedtemperature for the facial area of the individual, wherein the estimatedtemperature is based on conditions of an environment associated with theindividual; determine, based on the first temperature, the secondtemperature, and the estimated temperature, whether an appearance of thefacial area of the individual is likely altered by a face-alteringtechnology, wherein the first temperature corresponding to the secondtemperature or the estimated temperature indicates that the appearanceof the facial area of the individual is likely not altered by theface-altering technology, wherein the first temperature notcorresponding to the second temperature and the estimated temperatureindicates that the appearance of the facial area of the individual islikely altered by the face-altering technology; and selectively performfacial recognition on the facial area of the individual based on whetherthe appearance of the facial area of the individual is likely altered bythe face-altering technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2 .

FIGS. 4-6 are flow charts of example processes for detecting attempts todefeat facial recognition.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Facial recognition may be performed to authenticate an identity of anindividual, determine an identity of an individual of interest (e.g., anindividual suspected of committing a crime), identify an individual ofinterest (e.g., identify an individual from a crowd), and/or the like.In one example, a financial institution may perform facial recognitionon an individual when the individual is attempting to access an account.In particular, the financial institution may perform the facialrecognition in order to authenticate an identity of the individualbefore permitting the individual to access the account.

Sometimes, an individual may attempt to defeat facial recognition byaltering an appearance of the individual's face. For example, theindividual may wear a mask (e.g., a mask of another individual's face)or a facial prosthetic (e.g., a prosthetic nose) in an attempt to defeatfacial recognition. Masks and facial prosthetics have reached a level ofsophistication that makes detection of a mask or a facial prostheticdifficult and technically complex. As a result, a facial recognitionsystem may misidentify, or fail to identify, the individual that isattempting to defeat facial recognition. Thus, the facial recognitionsystem may waste resources (e.g., processor resources, memory resources,and/or the like) performing facial recognition on facial features of theindividual that are falsified or intentionally obscured. In addition,misidentification of the individual may permit the individual to engagein illegal activity, such as fraudulently accessing a financial accountthat belongs to another individual. In such a case, a financialinstitution that maintains the financial account may consume resources(e.g., computing resources and/or network resources) involved inidentifying, investigating, and/or correcting the illegal activity.

Some implementations described herein provide a detection platform thatmay detect an attempt to defeat facial recognition using a face-alteringtechnology (e.g., a mask, a facial prosthetic, and/or the like). Thedetection platform may identify a facial area of an individual that is acandidate for authentication by facial recognition, and may identify acharacteristic of the facial area. For example, the characteristic maybe a temperature of the facial area, a level of perspiration of thefacial area, a depth of an eye recess of the facial area, and/or thelike. Based on the characteristic, the detection platform may determinewhether an appearance of the facial area is likely altered, and mayselectively perform facial recognition on the individual based onwhether the appearance of the facial area is likely altered. Forexample, the detection platform may not perform facial recognition onthe individual based on determining that the appearance of the facialarea is likely altered.

In this way, the detection platform conserves computing resourcesinvolved in facial recognition that would otherwise be wasted if facialrecognition was performed on individuals using a face-alteringtechnology (e.g., because facial recognition is unlikely to accuratelyidentify such individuals). Moreover, the detection platform can reduceinstances of misidentification caused by face-altering technology (e.g.,masks), thereby conserving computing resources involved in identifying,investigating, and/or correcting illegal activity that may otherwise bepermitted by misidentification.

FIGS. 1A and 1B are diagrams of one or more example implementations 100described herein. As shown in FIGS. 1A and 1B, example implementation(s)100 may include a detection platform. The detection platform may beassociated with an entity that performs facial recognition, such as afinancial institution, a government agency (e.g., a police department, atransportation security agency, and/or the like), a school, a stadium,and/or the like. In some implementations, the detection platform may beassociated with a financial institution that performs facial recognitionin order to authenticate an individual to access a secure area. Thesecure area may be an account maintained by the financial institutionthat may be accessed via a transaction device, such as an automatedteller machine (ATM) device.

The detection platform may be associated with one or more cameras and/orone or more sensors. The one or more cameras may be configured tocapture an image or a video of an individual. The one or more sensorsmay be configured to detect and report one or more measurable parametersof the individual. For example, the one or more sensors may detect atemperature associated with the individual, a level of moistureassociated with the individual, and/or the like. In someimplementations, the detection platform may include the one or morecameras and/or the one or more sensors. Additionally, or alternatively,another device that is accessible to the detection platform may includethe one or more cameras and/or the one or more sensors. For example, atransaction device (e.g., an ATM device) that is in communication withthe detection platform may include the one or more cameras and/or theone or more sensors, and may provide data obtained by the one or morecameras and/or the one or more sensors to the detection platform.

The detection platform may implement a facial recognition system. Thefacial recognition system may utilize a facial recognition technique toidentify a face of an individual. For example, the facial recognitiontechnique may identify facial features (e.g., eyes, nose, mouth, jaw,cheekbones, and/or the like) of the individual from an image of theindividual's face, and may determine a data set that represents arelative position, size, and/or shape of the facial features. The dataset may be compared to other data sets representing facial features ofknown individuals in order to identify the individual. The data setsrepresenting facial features of known individuals may be stored in adata structure (e.g., a data repository, a database, a table, a list,and/or the like) of the detection platform or accessible to thedetection platform.

As shown in FIG. 1A, and by reference number 105, the detection platformmay select an individual that is a candidate for authentication byfacial recognition. In some cases, the individual may be utilizing aface-altering technology, such as a mask (e.g., a latex mask), a facialprosthetic, a bandage, an eye patch, and/or the like.

In some implementations, the detection platform (e.g., using a camera)may select the individual as the candidate for authentication by facialrecognition if the individual enters a particular area (e.g., a bank, astadium, an airport, and/or the like). For example, the detectionplatform may select the individual if the individual enters a particulararea associated with a transaction device, such as a checkout area of amerchant, an ATM vestibule, and/or the like. In such a case, theparticular area may include a proximity (e.g., 10 feet, 25 feet, 50feet, and/or the like) of the transaction device. Thus, the detectionplatform may select the individual as the candidate for facialrecognition if the individual is approaching the transaction device(e.g., enters the proximity). In some implementations, the detectionplatform may distinguish (e.g., utilizing a machine learning model)between individuals entering the proximity in order to use thetransaction device and those merely passing through, based on a behaviorof an individual, such as a direction of travel of the individual, adirection of an eye gaze of the individual, whether the individual isreaching into a bag or a pocket, and/or the like.

In some implementations, the detection platform may select theindividual as the candidate for authentication by facial recognition ifthe individual requests access to a secure area. For example, thedetection platform may select the individual if the individual requestsaccess to an account (e.g., by inserting a transaction card into atransaction device, swiping a transaction card at a transaction device,and/or the like). As another example, the detection platform may selectthe individual if the individual requests access to a physical area(e.g., by entering an access code or presenting a credential at anaccess control system of the physical area).

As shown by reference number 110, the detection platform may identify afacial area of the individual selected as the candidate for facialrecognition. For example, the detection platform may capture, or obtain,an image (e.g., a photograph or a video frame of a video) of theindividual in order to identify the facial area of the individual fromthe image. In some implementations, a camera of a transaction device(e.g., an ATM device) may capture an image of the individual and providethe image to the detection platform. Additionally, or alternatively, acamera associated with a physical area (e.g., an ATM vestibule, anairport, a stadium, and/or the like) may capture an image of theindividual and provide the image to the detection platform.

The detection platform may process the image of the individual toidentify the facial area of the individual. For example, the detectionplatform may process the image using an artificial intelligencetechnique, such as machine learning, deep learning, and/or the like. Forexample, the detection platform may use a machine learning (e.g.,computer vision) model to identify the facial area. For example, thedetection platform may train the machine learning model based on one ormore parameters associated with a facial area, such as shape, color,arrangement of features, and/or the like. The detection platform maytrain the machine learning model, according to the one or moreparameters, using historical data associated with faces (e.g., images offaces). Using the historical data and the one or more parameters asinputs to the machine learning model, the detection platform may trainthe machine learning model to identify the facial area from an image ofthe individual. In some implementations, the detection platform mayobtain and utilize a machine learning model that was trained by anotherdevice.

In some implementations, the detection platform may identify one or moreother bodily areas of the individual from an image of the individual, ina manner similar to that described above. For example, the detectionplatform may identify an area of exposed skin of the individual that isnot associated with the facial area of the individual. The area ofexposed skin may relate to an ear, a neck, an arm, a hand, a leg, and/orthe like of the individual.

As shown in FIG. 1B, and by reference number 115, the detection platformmay identify a characteristic of the facial area of the individual. Thecharacteristic may be unrelated to a facial feature (e.g., unrelated toeyes, a nose, a mouth, a jaw, and/or the like) of the facial area. Forexample, the characteristic may relate to a temperature of the facialarea or a level of moisture (e.g., perspiration) of the facial area.

In some implementations, the detection platform may obtain thetemperature of the facial area. In such a case, the detection platformmay obtain the temperature of the facial area from a measurement of atemperature sensor. For example, the detection platform may obtain thetemperature of the facial area from a measurement of an infrared (IR)sensor. In such a case, after identifying the facial area, the detectionplatform may cause the IR sensor to measure a level of IR lightassociated with the facial area. The detection platform may convert themeasured level of IR light to a temperature. In some implementations,the IR sensor may be associated with an IR light source, and the IRsensor may measure a level of IR light from the IR light source that isreflected off the facial area.

In some implementations, the IR sensor and/or the IR light source may bestationary, and positioned so as to be directed to the facial area ofthe individual (e.g., positioned so as to be directed to the facial areaof the individual when the individual is using an ATM device, enteringan ATM vestibule, entering an airport, and/or the like). Alternatively,the IR sensor and/or the IR light source may be configured to changeposition to permit the IR sensor and/or the IR light source to bedirected at the facial area of the individual during a movement of theindividual.

In some implementations, the detection platform may include the IRsensor and/or the IR light source. Additionally, or alternatively,another device that is accessible to the detection platform may includethe IR sensor and/or the IR light source. For example, a transactiondevice (e.g., an ATM device) that is in communication with the detectionplatform may include the IR sensor and/or the IR light source, and mayprovide IR light measurements obtained by the IR sensor to the detectionplatform.

In some implementations, the detection platform may obtain the level ofmoisture (e.g., perspiration) of the facial area. In such a case, thedetection platform may obtain the level of moisture using a machinelearning (e.g., computer vision) model in a manner similar to thatdescribed above. For example, the detection platform may train themachine learning model to identify perspiration from an image of thefacial area of the individual. In some implementations, the detectionplatform may obtain and utilize a machine learning model that wastrained by another device. The machine learning model may determine thelevel of moisture as a total area of the facial area where perspirationis identified, a percentage area of the facial area where perspirationis identified, and/or the like.

Additionally, or alternatively, the detection platform may obtain thelevel of moisture from an IR sensor, such as an IR sensor as describedabove. For example, the IR sensor may take IR light measurements, whichmay be converted to temperature measurements, from a plurality oflocations on the facial area of the individual. Locations whereperspiration is present may have lower temperatures than locations whereperspiration is not present. Accordingly, the detection platform mayidentify a location as being associated with perspiration when thelocation has a temperature that does not satisfy a particular threshold(e.g., a threshold based on temperature measurements from the pluralityof locations). In addition, the detection platform may determine thelevel of moisture as a total area of the facial area where perspirationis identified, a percentage area of the facial area where perspirationis identified, and/or the like.

Additionally, or alternatively, the detection platform may obtain thelevel of moisture from a galvanometer that measures a galvanic skinresponse (i.e., a change in electrical resistance of skin caused byperspiration). The detection platform may determine the level ofmoisture by converting a level of electrical resistance measured by thegalvanometer. The detection platform, or another device accessible tothe detection platform (e.g., an ATM device), may include a galvanometerin order to measure a galvanic skin response of the individual. In sucha case, the detection platform, or the other device, may instruct theindividual to place the individual's facial area in contact with thegalvanometer in order to measure a galvanic skin response of theindividual's facial area.

In some implementations, the detection platform also may identify acharacteristic of the area of exposed skin of the individual (e.g., anarea of exposed skin of an ear, a neck, a hand, and/or the like of theindividual). For example, the detection platform may obtain atemperature or a level of moisture of the area of exposed skin in amanner similar to that described above.

In some implementations, the identified characteristic of the facialarea of the individual may be a recess associated with an eye of theindividual. The detection platform may obtain a measurement of therecess using a machine learning (e.g., computer vision) model, in amanner similar to that described above. For example, the detectionplatform may train the machine learning model to determine a recess ofan eye from an image of the facial area of the individual. In someimplementations, the detection platform may obtain and utilize a machinelearning model that was trained by another device.

As shown by reference number 120, the detection platform may determinewhether an appearance of the facial area of the individual is likelyaltered by a face-altering technology (e.g., a mask, a facialprosthetic, and/or the like). For example, the detection platform maydetermine, based on the identified characteristic of the facial areaand/or the identified characteristic of the area of exposed skin,whether the appearance of the facial area is likely altered by aface-altering technology.

In some implementations, the detection platform may compare theidentified characteristic of the facial area and the identifiedcharacteristic of the area of exposed skin in order to determine whetherthe appearance of the facial area is likely altered. For example, thedetection platform may compare the temperature of the facial area andthe temperature of the area of exposed skin. Since a face-alteringtechnology may reduce heat that is emitted or reflected by the facialarea relative to the area of exposed skin, the detection platform maydetermine that the appearance of the facial area is likely altered whenthe temperature of the facial area and the temperature of the area ofexposed skin do not correspond (e.g., the temperature of the facial areais outside of a threshold range, such as ±1%, ±5%, and/or the like, ofthe temperature of the area of exposed skin). Conversely, the detectionplatform may determine that the appearance of the facial area is likelynot altered when the temperature of the facial area and the temperatureof the area of exposed skin correspond.

As another example, the detection platform may compare the level ofmoisture (e.g., perspiration) of the facial area and the level ofmoisture of the area of exposed skin. Since a face-altering technologymay obstruct perspiration present on the facial area, the detectionplatform may determine that the appearance of the facial area is likelyaltered when the level of moisture of the facial area and the level ofmoisture of the area of exposed skin do not correspond (e.g., the levelof moisture of the facial area is outside of a threshold range, such as±1%, ±5%, and/or the like, of the level of moisture of the area ofexposed skin). Conversely, the detection platform may determine that theappearance of the facial area is likely not altered when the level ofmoisture of the facial area and the level of moisture of the area ofexposed skin correspond.

In some implementations, the detection platform may compare theidentified characteristic of the facial area to an estimated value forthe characteristic of the facial area. For example, the detectionplatform may compare the temperature of the facial area to an estimatedtemperature for the facial area. The detection platform may utilize amachine learning model to determine the estimated temperature, in amanner similar to that described above. For example, the detectionplatform may train the machine learning model based on one or moreparameters associated with a facial temperature, such as an outdoorstemperature, a location of an individual (e.g., indoors or outdoors(e.g., a transaction device, such as an ATM device, may be locatedindoors or outdoors)), an indoors temperature, an amount of exposed skinof an individual, and/or the like. The detection platform may train themachine learning model, according to the one or more parameters, usinghistorical data associated with facial temperatures. Using thehistorical data and the one or more parameters as inputs to the machinelearning model, the detection platform may train the machine learningmodel to determine an estimated facial temperature for the facial areaof the individual. In some implementations, the detection platform mayobtain and utilize a machine learning model that was trained by anotherdevice. Since a face-altering technology may reduce heat that is emittedor reflected by the facial area, the detection platform may determinethat the appearance of the facial area is likely altered when thetemperature of the facial area and the estimated temperature for thefacial area do not correspond (e.g., the temperature of the facial areais outside of a threshold range, such as ±1%, ±5%, and/or the like, ofthe estimated temperature for the facial area). Conversely, thedetection platform may determine that the appearance of the facial areais likely not altered when the temperature of the facial area and theestimated temperature for the facial area correspond.

As another example, the detection platform may compare the level ofmoisture of the facial area to an estimated level of moisture for thefacial area. The detection platform may utilize a machine learning modelto determine the estimated moisture level, in a manner similar to thatdescribed above. Since a face-altering technology may obstructperspiration present on the facial area, the detection platform maydetermine that the appearance of the facial area is likely altered whenthe level of moisture of the facial area and the estimated level ofmoisture for the facial area do not correspond (e.g., the level ofmoisture of the facial area is outside of a threshold range, such as±1%, ±5%, and/or the like, of the estimated level of moisture for thefacial area). Conversely, the detection platform may determine that theappearance of the facial area is likely not altered when the level ofmoisture of the facial area and the estimated level of moisture for thefacial area correspond.

In some implementations, the detection platform may determine whetherthe appearance of the facial area of the individual is likely altered bya face-altering technology based on the measurement of the recess of theeye of the individual. For example, the detection platform may determinewhether the measurement of the recess exceeds a threshold valueassociated with a normal recess of an eye. The threshold value may bebased on an aggregate (e.g., average) measurement determined from aplurality of individuals (e.g., a plurality of individuals having one ormore facial features that are similar to those of the individual). Sincea face-altering technology, such as a mask, may protrude from theindividual's face, thereby causing a deeper eye recess, the detectionplatform may determine that the appearance of the facial area is likelyaltered when the measurement of the recess of the eye of the individualexceeds the threshold value. Conversely, the detection platform maydetermine that the appearance of the facial area is likely not alteredwhen the measurement of the recess of the eye of the individual does notexceed the threshold value.

In some implementations, the detection platform may employ anycombination of the foregoing techniques for determining whether theappearance of the facial area of the individual is likely altered by aface-altering technology. For example, the detection platform maydetermine whether the appearance of the facial area is likely alteredbased on any one or more of whether a temperature of the facial areacorresponds to a temperature of the area of exposed skin, whether alevel of moisture of the facial area corresponds to a level of moistureof the area of exposed skin, whether a temperature of the facial areacorresponds to an estimated temperature for the facial area, whether alevel of moisture of the facial area corresponds to an estimated levelof moisture for the facial area, or whether a recess of an eye of theindividual exceeds a threshold value.

In some implementations, the detection platform may determine whetherthe appearance of the facial area of the individual is likely altered bya face-altering technology prior to performing facial recognition on theindividual. For example, the detection platform may determine whetherthe appearance of the facial area is likely altered before theindividual requests access to a secure area, to thereby determinewhether facial recognition is to be performed on the individual. Forexample, the detection platform may determine whether the appearance ofthe facial area is likely altered when the individual is approaching atransaction device (e.g., before the individual requests access to asecure area via the transaction device). In this way, the detectionplatform may determine a manner in which the individual is to beauthenticated before the individual requests access to a secure area,thereby improving a speed and an efficiency of an authenticationprocess.

As shown by reference number 125, the detection platform may selectivelyperform facial recognition on the facial area of the individual based onwhether the appearance of the facial area is likely altered by aface-altering technology. For example, the detection platform maydetermine not to perform facial recognition on the facial area based ondetermining that the appearance of the facial area is likely altered. Insuch a case, the detection platform may determine that the individual isto be authenticated according to a technique that does not includefacial recognition. For example, the detection platform may prompt, orcause another device to prompt, the individual to provide a password, apersonal identification number (PIN), a biometric identifier, and/or thelike based on determining that the appearance of the facial area islikely altered. In this way, the detection platform conserves computingresources associated with facial recognition by determining not toperform facial recognition when facial recognition is unlikely toproduce an accurate result (e.g., when the individual is utilizing aface-altering technology).

In some implementations, based on determining that the appearance of thefacial area is likely altered, the detection platform may administer, orcause another device to administer, a facial expression challenge to theindividual. For example, the detection platform, or the other device,may request that the individual perform one or more facial expressions,such as smiling, frowning, pouting, raising one or both eyebrows, and/orthe like. Continuing with the previous example, the detection platform,or the other device, may determine whether the performed facialexpressions indicate that the appearance of the facial area of theindividual is likely altered by a face-altering technology. This mayinclude determining (e.g., using a machine learning model) whether arelative position, size, shape, and/or the like of the performed facialexpressions correspond to reference facial expressions (e.g., facialexpressions performed by one or more individuals not utilizing aface-altering technology). Based on determining that one or more of theperformed facial expressions do not correspond to the reference facialexpressions, the detection platform, or the other device, may determinethat the individual is not to be authenticated according to facialrecognition.

In some implementations, the detection platform may perform one or moreactions based on determining that the appearance of the facial area ofthe individual is likely altered by a face-altering technology. Forexample, if the individual is attempting to access a secure area (e.g.,an account), the detection platform may lock the secure area (e.g., fora time period), require one or more additional authentication factors toaccess the secure area (e.g., for a time period), transmit anotification to a user device of an owner of the secure area indicatingsuspicious activity, transmit a notification to a user device of a lawenforcement agency indicating suspicious activity, and/or the like. Asanother example, the detection platform may perform facial recognitionon the facial area of the individual in order to determine an identityof a person being impersonated by the individual, and may lock one ormore accounts associated with the person, require one or more additionalauthentication factors to access the one or more accounts, transmit anotification to a user device of the person indicating an impersonationattempt, transmit a notification to a user device of a law enforcementagency indicating an impersonation attempt, and/or the like.

In some implementations, the detection platform may determine toauthenticate the individual according to facial recognition based ondetermining that the appearance of the facial area of the individual islikely not altered by a face-altering technology. In such a case, thedetection platform may utilize a facial recognition system, as describedabove, to determine an identity of the individual based on an image ofthe individual. In some implementations, the detection platform maydetermine whether the identity matches that of an owner of an accountthe individual was attempting to access, an owner of a credential thatthe individual was using, and/or the like to thereby authenticate theindividual. In this way, the detection platform performs facialrecognition when facial recognition is likely to produce an accurateresult (e.g., when the individual is not utilizing a face-alteringtechnology), thereby conserving computing resources and reducinginstances of misidentification as well as illegal activity that may bepermitted by misidentification.

As indicated above, FIGS. 1A and 1B are provided as one or moreexamples. Other examples may differ from what is described with regardto FIGS. 1A and 1B.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods described herein may be implemented. As shown in FIG. 2 ,environment 200 may include a transaction device 210, a camera 220, asensor 230, a detection platform 240, a computing resource 245, a cloudcomputing environment 250, and a network 260. Devices of environment 200may interconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

Transaction device 210 includes one or more devices capable ofreceiving, generating, storing, processing, and/or providing informationrelating to a transaction (e.g., a transaction relating to a cashwithdrawal, a use of a transaction card, and/or the like). For example,transaction device 210 may include an ATM device, a point of sale (POS)device, a kiosk device, and/or the like. An ATM device may include anelectronic telecommunications device that enables customers of financialinstitutions to perform financial transactions, such as cashwithdrawals, deposits, transferring funds, obtaining accountinformation, and/or the like, at any time and without direct interactionwith employees of the financial institutions. A POS device may includean electronic device used to process transaction card payments at aretail location. The POS device may read information from a transactioncard (e.g., a credit card, a debit card, a gift card, and/or the like),and may determine whether there are sufficient funds in an accountassociated with the transaction card for a transaction. The POS devicemay cause a transfer of funds from the account associated with thetransaction card to an account of a retailer and may record thetransaction. A kiosk device may include a computer terminal featuringspecialized hardware and software that provides access to informationand/or applications for communication, commerce, entertainment,education, and/or the like.

Camera 220 includes one or more devices capable of capturing video data,an image, and/or the like. For example, camera 220 may include a videocamera, a still image camera, an infrared camera, and/or the like. Insome implementations, camera 220 may capture an image of an individualand may provide the image to detection platform 240, as describedelsewhere herein. In some implementations, camera 220 may process animage in a manner that is the same as or similar to that describedelsewhere herein, and may provide a result of processing the image totransaction device 210 or detection platform 240 for further processing,for analysis, and/or the like, as described elsewhere herein.

Sensor 230 includes one or more devices capable of detecting andreporting data relating to a measurable parameter of an individual thatis a candidate for authentication by facial recognition. For example,sensor 230 may include a temperature sensor, an infrared sensor, amoisture sensor, and/or the like. In some implementations, sensor 230may process the data in a manner that is the same as or similar to thatdescribed elsewhere herein, and may provide a result of processing thedata to transaction device 210 or detection platform 240 for furtherprocessing, for analysis, and/or the like, as described elsewhereherein.

Detection platform 240 includes one or more computing resources assignedto determine whether an appearance of a facial area of an individual islikely altered by a face-altering technology. For example, detectionplatform 240 may be a platform implemented by cloud computingenvironment 250 that may select an individual that is a candidate forfacial recognition, identify a facial area of the individual, identify acharacteristic of the facial area, determine whether an appearance ofthe facial area is likely altered by a face-altering technology, performfacial recognition on the facial area, and/or the like. In someimplementations, detection platform 240 is implemented by computingresources 245 of cloud computing environment 250.

Detection platform 240 may include a server device or a group of serverdevices. In some implementations, detection platform 240 may be hostedin cloud computing environment 250. Notably, while implementationsdescribed herein may describe detection platform 240 as being hosted incloud computing environment 250, in some implementations, detectionplatform 240 may be non-cloud-based or may be partially cloud-based. Forexample, in some implementations, detection platform 240 may beimplemented by transaction device 210.

Cloud computing environment 250 includes an environment that deliverscomputing as a service, whereby shared resources, services, and/or thelike may be provided to transaction device 210, camera 220, sensor 230,and/or the like. Cloud computing environment 250 may providecomputation, software, data access, storage, and/or other services thatdo not require end-user knowledge of a physical location andconfiguration of a system and/or a device that delivers the services. Asshown, cloud computing environment 250 may include detection platform240 and computing resource 245.

Computing resource 245 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource245 may host detection platform 240. The cloud resources may includecompute instances executing in computing resource 245, storage devicesprovided in computing resource 245, data transfer devices provided bycomputing resource 245, and/or the like. In some implementations,computing resource 245 may communicate with other computing resources245 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 2 , computing resource 245 may include a groupof cloud resources, such as one or more applications (“APPs”) 245-1, oneor more virtual machines (“VMs”) 245-2, virtualized storage (“VSs”)245-3, one or more hypervisors (“HYPs”) 245-4, or the like.

Application 245-1 includes one or more software applications that may beprovided to or accessed by transaction device 210, camera 220, sensor230, and/or the like. Application 245-1 may eliminate a need to installand execute the software applications on transaction device 210, camera220, sensor 230, and/or the like. For example, application 245-1 mayinclude software associated with detection platform 240 and/or any othersoftware capable of being provided via cloud computing environment 250.In some implementations, one application 245-1 may send/receiveinformation to/from one or more other applications 245-1, via virtualmachine 245-2.

Virtual machine 245-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 245-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 245-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 245-2 may execute on behalf of a user,and may manage infrastructure of cloud computing environment 250, suchas data management, synchronization, or long-duration data transfers.

Virtualized storage 245-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 245. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 245-4 provides hardware virtualization techniques that allowmultiple operating systems (e.g., “guest operating systems”) to executeconcurrently on a host computer, such as computing resource 245.Hypervisor 245-4 may present a virtual operating platform to the guestoperating systems and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 260 includes one or more wired and/or wireless networks. Forexample, network 260 may include a cellular network (e.g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, and/or the like), a public land mobile network(PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the PublicSwitched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, or the like, and/or a combination of these or othertypes of networks.

The quantity and arrangement of devices and networks shown in FIG. 2 areprovided as one or more examples. In practice, there may be additionaldevices and/or networks, fewer devices and/or networks, differentdevices and/or networks, or differently arranged devices and/or networksthan those shown in FIG. 2 . Furthermore, two or more devices shown inFIG. 2 may be implemented within a single device, or a single deviceshown in FIG. 2 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 200 may perform one or more functions describedas being performed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to transaction device 210, camera 220, sensor 230,detection platform 240, and/or computing resource 245. In someimplementations, transaction device 210, camera 220, sensor 230,detection platform 240, and/or computing resource 245 may include one ormore devices 300 and/or one or more components of device 300. As shownin FIG. 3 , device 300 may include a bus 310, a processor 320, a memory330, a storage component 340, an input component 350, an outputcomponent 360, and a communication interface 370.

Bus 310 includes a component that permits communication among multiplecomponents of device 300. Processor 320 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 320is a central processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RANI), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 360 includes a component thatprovides output information from device 300 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 300 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 370 may permit device300 to receive information from another device and/or provideinformation to another device. For example, communication interface 370may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a Wi-Fi interface, a cellularnetwork interface, and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The quantity and arrangement of components shown in FIG. 3 are providedas an example. In practice, device 300 may include additionalcomponents, fewer components, different components, or differentlyarranged components than those shown in FIG. 3 . Additionally, oralternatively, a set of components (e.g., one or more components) ofdevice 300 may perform one or more functions described as beingperformed by another set of components of device 300.

FIG. 4 is a flow chart of an example process 400 for detecting attemptsto defeat facial recognition. In some implementations, one or moreprocess blocks of FIG. 4 may be performed by a detection platform (e.g.,detection platform 240). In some implementations, one or more processblocks of FIG. 4 may be performed by another device or a group ofdevices separate from or including the detection platform, such as atransaction device (e.g., transaction device 210), a camera (e.g.,camera 220), a sensor (e.g., sensor 230), and/or the like.

As shown in FIG. 4 , process 400 may include selecting an individualthat is a candidate for authentication by facial recognition (block410). For example, the detection platform (e.g., using camera 220,computing resource 245, processor 320, memory 330, storage component340, input component 350, communication interface 370, and/or the like)may select an individual that is a candidate for authentication byfacial recognition, as described above.

As further shown in FIG. 4 , process 400 may include identifying, basedon selecting the individual, a facial area of the individual and an areaof exposed skin of the individual, wherein the area of exposed skin ofthe individual is not associated with the facial area of the individual(block 420). For example, the detection platform (e.g., using camera220, computing resource 245, processor 320, memory 330, storagecomponent 340, and/or the like) may identify, based on selecting theindividual, a facial area of the individual and an area of exposed skinof the individual, as described above. In some implementations, the areaof exposed skin of the individual is not associated with the facial areaof the individual.

As further shown in FIG. 4 , process 400 may include obtaining a firsttemperature associated with the facial area of the individual and asecond temperature associated with the area of exposed skin of theindividual (block 430). For example, the detection platform (e.g., usingsensor 230, computing resource 245, processor 320, memory 330, storagecomponent 340, input component 350, communication interface 370, and/orthe like) may obtain a first temperature associated with the facial areaof the individual and a second temperature associated with the area ofexposed skin of the individual, as described above.

As further shown in FIG. 4 , process 400 may include determining, basedon the first temperature and the second temperature, whether anappearance of the facial area of the individual is likely altered by aface-altering technology, wherein the first temperature corresponding tothe second temperature indicates that the appearance of the facial areaof the individual is likely not altered by the face-altering technology,and wherein the first temperature not corresponding to the secondtemperature indicates that the appearance of the facial area of theindividual is likely altered by the face-altering technology (block440). For example, the detection platform (e.g., using computingresource 245, processor 320, memory 330, storage component 340, and/orthe like) may determine, based on the first temperature and the secondtemperature, whether an appearance of the facial area of the individualis likely altered by a face-altering technology, as described above. Insome implementations, the first temperature corresponding to the secondtemperature indicates that the appearance of the facial area of theindividual is likely not altered by the face-altering technology. Insome implementations, the first temperature not corresponding to thesecond temperature indicates that the appearance of the facial area ofthe individual is likely altered by the face-altering technology.

As further shown in FIG. 4 , process 400 may include selectivelyperforming facial recognition on the facial area of the individual basedon whether the appearance of the facial area of the individual is likelyaltered by the face-altering technology (block 450). For example, thedetection platform (e.g., using computing resource 245, processor 320,memory 330, storage component 340, and/or the like) may selectivelyperform facial recognition on the facial area of the individual based onwhether the appearance of the facial area of the individual is likelyaltered by the face-altering technology, as described above.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, selecting the individual that is thecandidate for authentication by facial recognition may include selectingthe individual that is the candidate for authentication by facialrecognition based on a proximity of the individual to a transactiondevice.

In a second implementation, alone or in combination with the firstimplementation, process 400 may further include determining a firstperspiration level on the facial area of the individual and determininga second perspiration level on the area of exposed skin of theindividual, where the first temperature corresponding to the secondtemperature and the first perspiration level corresponding to the secondperspiration level indicates that the appearance of the facial area ofthe individual is likely not altered by the face-altering technology,and where the first temperature not corresponding to the secondtemperature or the first perspiration level not corresponding to thesecond perspiration level indicates that the appearance of the facialarea of the individual is likely altered by the face-alteringtechnology. In a third implementation, alone or in combination with oneor more of the first and second implementations, the first perspirationlevel and the second perspiration level may be determined by one or moreof computer vision, infrared moisture detection, or galvanic skinresponse detection.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, process 400 may further includedetermining that the appearance of the facial area of the individual islikely not altered by the face-altering technology, and performingauthentication of the individual using facial recognition, oralternatively, determining that the appearance of the facial area of theindividual is likely altered by the face-altering technology, andperforming authentication of the individual using a technique that doesnot include facial recognition.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, the face-altering technologymay be at least one of a mask, a facial prosthetic, a bandage, or aneyepatch.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4 . Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for detecting attemptsto defeat facial recognition. In some implementations, one or moreprocess blocks of FIG. 5 may be performed by a detection platform (e.g.,detection platform 240). In some implementations, one or more processblocks of FIG. 5 may be performed by another device or a group ofdevices separate from or including the detection platform, such as atransaction device (e.g., transaction device 210), a camera (e.g.,camera 220), a sensor (e.g., sensor 230), and/or the like.

As shown in FIG. 5 , process 500 may include selecting an individualthat is a candidate for authentication by facial recognition (block510). For example, the detection platform (e.g., using camera 220,computing resource 245, processor 320, memory 330, storage component340, input component 350, communication interface 370, and/or the like)may select an individual that is a candidate for authentication byfacial recognition, as described above.

As further shown in FIG. 5 , process 500 may include obtaining atemperature associated with a facial area of the individual (block 520).For example, the detection platform (e.g., using sensor 230, computingresource 245, processor 320, memory 330, storage component 340, inputcomponent 350, communication interface 370, and/or the like) may obtaina temperature associated with a facial area of the individual, asdescribed above.

As further shown in FIG. 5 , process 500 may include determining anestimated temperature for the facial area of the individual, wherein theestimated temperature is based on conditions of an environmentassociated with the individual (block 530). For example, the detectionplatform (e.g., using computing resource 245, processor 320, memory 330,storage component 340, and/or the like) may determine an estimatedtemperature for the facial area of the individual, as described above.In some implementations, the estimated temperature is based onconditions of an environment associated with the individual.

As further shown in FIG. 5 , process 500 may include determining, basedon the temperature and the estimated temperature, whether an appearanceof the facial area of the individual is likely altered by aface-altering technology, wherein the temperature corresponding to theestimated temperature indicates that the appearance of the facial areaof the individual is likely not altered by the face-altering technology,and wherein the temperature not corresponding to the estimatedtemperature indicates that the appearance of the facial area of theindividual is likely altered by the face-altering technology (block540). For example, the detection platform (e.g., using computingresource 245, processor 320, memory 330, storage component 340, and/orthe like) may determine, based on the temperature and the estimatedtemperature, whether an appearance of the facial area of the individualis likely altered by a face-altering technology, as described above. Insome implementations, the temperature corresponding to the estimatedtemperature indicates that the appearance of the facial area of theindividual is likely not altered by the face-altering technology. Insome implementations, the temperature not corresponding to the estimatedtemperature indicates that the appearance of the facial area of theindividual is likely altered by the face-altering technology.

As further shown in FIG. 5 , process 500 may include selectivelyperforming facial recognition on the facial area of the individual basedon whether the appearance of the facial area of the individual is likelyaltered by the face-altering technology (block 550). For example, thedetection platform (e.g., using computing resource 245, processor 320,memory 330, storage component 340, and/or the like) may selectivelyperform facial recognition on the facial area of the individual based onwhether the appearance of the facial area of the individual is likelyaltered by the face-altering technology, as described above.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, obtaining the temperature associated with thefacial area of the individual and determining whether the temperaturecorresponds to the estimated temperature for the facial area of theindividual may include obtaining the temperature associated with thefacial area of the individual and determining whether the temperaturecorresponds to the estimated temperature for the facial area of theindividual before the individual is within a threshold distance from atransaction device.

In a second implementation, alone or in combination with the firstimplementation, process 500 may further include determining that theappearance of the facial area of the individual is likely not altered bythe face-altering technology, and performing authentication of theindividual using facial recognition, or alternatively, determining thatthe appearance of the facial area of the individual is likely altered bythe face-altering technology, and performing authentication of theindividual using a technique that does not include facial recognition.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, determining the estimatedtemperature for the facial area of the individual may includedetermining the estimated temperature for the facial area of theindividual using a machine learning model.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, process 500 may further includedetermining whether a recess associated with an eye of the individualsatisfies a threshold value, where the temperature corresponding to theestimated temperature and the recess associated with the eye satisfyingthe threshold value indicates that the appearance of the facial area ofthe individual is likely not altered by the face-altering technology,and where the temperature not corresponding to the estimated temperatureor the recess associated with the eye failing to satisfy the thresholdvalue indicates that the appearance of the facial area of the individualis likely altered by the face-altering technology.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, process 500 may furtherinclude determining that the appearance of the facial area of theindividual is likely altered by the face-altering technology, requestingthat the individual perform one or more facial expressions, determiningthat the one or more facial expressions indicate that the appearance ofthe facial area of the individual is likely altered by the face-alteringtechnology, and determining that the individual is not to beauthenticated according to facial recognition.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5 . Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for detecting attemptsto defeat facial recognition. In some implementations, one or moreprocess blocks of FIG. 6 may be performed by a detection platform (e.g.,detection platform 240). In some implementations, one or more processblocks of FIG. 6 may be performed by another device or a group ofdevices separate from or including the detection platform, such as atransaction device (e.g., transaction device 210), a camera (e.g.,camera 220), a sensor (e.g., sensor 230), and/or the like.

As shown in FIG. 6 , process 600 may include selecting an individualthat is a candidate for authentication by facial recognition (block610). For example, the detection platform (e.g., using camera 220,computing resource 245, processor 320, memory 330, storage component340, input component 350, communication interface 370, and/or the like)may select an individual that is a candidate for authentication byfacial recognition, as described above.

As further shown in FIG. 6 , process 600 may include identifying, basedon selecting the individual, a facial area of the individual and an areaof exposed skin of the individual, wherein the area of exposed skin ofthe individual is not associated with the facial area of the individual(block 620). For example, the detection platform (e.g., using camera220, computing resource 245, processor 320, memory 330, storagecomponent 340, and/or the like) may identify, based on selecting theindividual, a facial area of the individual and an area of exposed skinof the individual, as described above. In some implementations, the areaof exposed skin of the individual is not associated with the facial areaof the individual.

As further shown in FIG. 6 , process 600 may include obtaining a firsttemperature associated with the facial area of the individual and asecond temperature associated with the area of exposed skin of theindividual (block 630). For example, the detection platform (e.g., usingsensor 230, computing resource 245, processor 320, memory 330, storagecomponent 340, input component 350, communication interface 370, and/orthe like) may obtain a first temperature associated with the facial areaof the individual and a second temperature associated with the area ofexposed skin of the individual, as described above.

As further shown in FIG. 6 , process 600 may include determining anestimated temperature for the facial area of the individual, wherein theestimated temperature is based on conditions of an environmentassociated with the individual (block 640). For example, the detectionplatform (e.g., using computing resource 245, processor 320, memory 330,storage component 340, and/or the like) may determine an estimatedtemperature for the facial area of the individual, as described above.In some implementations, the estimated temperature is based onconditions of an environment associated with the individual.

As further shown in FIG. 6 , process 600 may include determining, basedon the first temperature, the second temperature, and the estimatedtemperature, whether an appearance of the facial area of the individualis likely altered by a face-altering technology, wherein the firsttemperature corresponding to the second temperature or the estimatedtemperature indicates that the appearance of the facial area of theindividual is likely not altered by the face-altering technology, andwherein the first temperature not corresponding to the secondtemperature and the estimated temperature indicates that the appearanceof the facial area of the individual is likely altered by theface-altering technology (block 650). For example, the detectionplatform (e.g., using computing resource 245, processor 320, memory 330,storage component 340, and/or the like) may determine, based on thefirst temperature, the second temperature, and the estimatedtemperature, whether an appearance of the facial area of the individualis likely altered by a face-altering technology, as described above. Insome implementations, the first temperature corresponding to the secondtemperature or the estimated temperature indicates that the appearanceof the facial area of the individual is likely not altered by theface-altering technology. In some implementations, the first temperaturenot corresponding to the second temperature and the estimatedtemperature indicates that the appearance of the facial area of theindividual is likely altered by the face-altering technology.

As further shown in FIG. 6 , process 600 may include selectivelyperforming facial recognition on the facial area of the individual basedon whether the appearance of the facial area of the individual is likelyaltered by the face-altering technology (block 660). For example, thedetection platform (e.g., using computing resource 245, processor 320,memory 330, storage component 340, and/or the like) may selectivelyperform facial recognition on the facial area of the individual based onwhether the appearance of the facial area of the individual is likelyaltered by the face-altering technology, as described above.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, selecting the individual that is thecandidate for authentication by facial recognition may include selectingthe individual that is the candidate for authentication by facialrecognition based on a request by the individual to access an account.

In a second implementation, alone or in combination with the firstimplementation, process 600 may further include determining that theappearance of the facial area of the individual is likely not altered bythe face-altering technology, and performing authentication of theindividual using facial recognition, or alternatively, determining thatthe appearance of the facial area of the individual is likely altered bythe face-altering technology, and performing authentication of theindividual using a technique that does not include facial recognition.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, identifying the facial area of theindividual and the area of exposed skin of the individual may includeidentifying the facial area of the individual and the area of exposedskin of the individual using computer vision.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, obtaining the first temperatureassociated with the facial area of the individual and the secondtemperature associated with the area of exposed skin of the individualmay include obtaining the first temperature associated with the facialarea of the individual and the second temperature associated with thearea of exposed skin of the individual from an infrared sensor.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6 . Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, more than thethreshold, higher than the threshold, greater than or equal to thethreshold, less than the threshold, fewer than the threshold, lower thanthe threshold, less than or equal to the threshold, equal to thethreshold, or the like.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, a combination of related and unrelated items,and/or the like), and may be used interchangeably with “one or more.”Where only one item is intended, the phrase “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise. Also, as used herein, the term “or”is intended to be inclusive when used in a series and may be usedinterchangeably with “and/or,” unless explicitly stated otherwise (e.g.,if used in combination with “either” or “only one of”).

What is claimed is:
 1. A method, comprising: identifying, by a device, afacial area of an individual and an area of exposed skin of theindividual, wherein the area of exposed skin of the individual is notassociated with the facial area of the individual; obtaining, by thedevice, a first temperature associated with the facial area of theindividual and a second temperature associated with the area of exposedskin of the individual; determining, by the device and based on acomparison of the first temperature and the second temperature, whetherthe first temperature is outside of a threshold difference intemperature range with respect to the second temperature; andselectively performing, by the device, facial recognition on the facialarea of the individual based on determining whether the firsttemperature is outside of the threshold difference in temperature rangewith respect to the second temperature.
 2. The method of claim 1,further comprising: identifying a characteristic of the facial area ofthe individual; determining, based on determining whether the firsttemperature is outside of the threshold difference in temperature rangewith respect to the second temperature, whether an appearance of thefacial area of the individual is likely altered by a face-alteringtechnology based on the characteristic; and wherein selectivelyperforming, by the device, facial recognition on the facial area of theindividual comprises: selectively performing, by the device, facialrecognition on the facial area of the individual based on whether theappearance of the facial area of the individual is likely altered by theface-altering technology.
 3. The method of claim 1, further comprising:selecting the individual based on a proximity of the individual to atransaction device.
 4. The method of claim 1, wherein identifying thefacial area of the individual and the area of exposed skin of theindividual comprises: identifying the facial area of the individual andthe area of exposed skin of the individual using machine learning. 5.The method of claim 1, wherein obtaining the first temperature and thesecond temperature comprises: obtaining the first temperature and thesecond temperature from a temperature sensor.
 6. The method of claim 1,further comprising: obtaining data identifying an actual level ofmoisture of the facial area; and wherein selectively performing thefacial recognition on the facial area of the individual comprises:selectively performing the facial recognition on the facial area of theindividual based on a comparison of the actual level of moisture to anestimated level of moisture.
 7. The method of claim 1, furthercomprising: selecting the individual based on a credential associatedwith the individual.
 8. A device, comprising: one or more memories; andone or more processors, communicatively coupled to the one or morememories, configured to: identify a facial area of an individual and anarea of skin of the individual, wherein the area of skin of theindividual is not associated with the facial area of the individual;obtain a first temperature associated with the facial area of theindividual and a second temperature associated with the area of skin ofthe individual; determine whether the first temperature is outside of athreshold difference in temperature range with respect to the secondtemperature; and selectively perform facial recognition on the facialarea of the individual based on determining whether the firsttemperature is outside of the threshold difference in temperature rangewith respect to the second temperature.
 9. The device of claim 8,wherein the one or more processors are further configured to: determinethat the facial area of the individual is likely not altered byface-altering technology; and wherein the one or more processors, whenselectively performing facial recognition on the facial area of theindividual, are configured to: perform authentication of the individualusing facial recognition.
 10. The device of claim 8, wherein the one ormore processors are further configured to: determine that the facialarea of the individual is likely altered by face-altering technology;and wherein the one or more processors, when selectively performingfacial recognition on the facial area of the individual, are to: performauthentication of the individual using a technique that does not includefacial recognition.
 11. The device of claim 8, wherein the one or moreprocessors are further configured to: obtain data identifying an actuallevel of moisture of the facial area; and wherein the one or moreprocessors, when selectively performing facial recognition on the facialarea of the individual, are to: selectively perform the facialrecognition on the facial area of the individual based on a comparisonof the actual level of moisture to an estimated level of moisture. 12.The device of claim 8, wherein the one or more processors are furtherconfigured to: determine, based on determining whether the firsttemperature is outside of the threshold difference in temperature rangewith respect to the second temperature, whether an appearance of thefacial area of the individual is likely altered by a face-alteringtechnology; and wherein the one or more processors, when selectivelyperforming facial recognition on the facial area of the individual, areto: selectively perform facial recognition on the facial area of theindividual based on whether the appearance of the facial area of theindividual is likely altered by the face-altering technology.
 13. Thedevice of claim 8, wherein the one or more processors, when obtainingthe first temperature and the second temperature, are configured to:obtain the first temperature and the second temperature from atemperature sensor.
 14. The device of claim 8, wherein the one or moreprocessors are further configured to: select the individual that is acandidate for authentication by facial recognition based on a proximityof the individual to the device.
 15. A non-transitory computer-readablemedium storing instructions, the instructions comprising: one or moreinstructions that, when executed by one or more processors, cause theone or more processors to: identify a facial area of an individual andan area of exposed skin of the individual, wherein the area of exposedskin of the individual is not associated with the facial area of theindividual; obtain a first temperature associated with the facial areaof the individual and a second temperature associated with the area ofexposed skin of the individual; and selectively perform facialrecognition on the facial area of the individual based on determiningwhether the first temperature is outside of a threshold difference intemperature range with respect to the second temperature.
 16. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, when executed by the one or more processors, furthercause the one or more processors to: select the individual based on acredential associated with the individual.
 17. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: select the individual by facialrecognition based on a request by the individual to access one or moreof: an account, or a physical area.
 18. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: determine that an appearance of thefacial area of the individual is likely altered by face-alteringtechnology; and perform authentication of the individual using atechnique that does not include facial recognition.
 19. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, that cause the one or more processors to identify thefacial area of the individual and the area of exposed skin of theindividual, cause the one or more processors to: identify the facialarea of the individual and the area of exposed skin of the individualusing a camera.
 20. The non-transitory computer-readable medium of claim15, wherein the one or more instructions, when executed by the one ormore processors, further cause the one or more processors to: determinethat the facial area of the individual is likely altered byface-altering technology; and wherein the one or more instructions, thatcause the one or more processors to selectively perform facialrecognition on the facial area of the individual, cause the one or moreprocessors to: perform authentication of the individual using atechnique that does not include facial recognition.